Metainformation is a common companion to biomedical images. However, this potentially powerful additional source of signal from image acquisition has had limited use in deep learning methods, for semantic segmentation in particular. Here, we incorporate metadata by employing a channel modulation mechanism in convolutional networks and study its effect on semantic segmentation tasks. We demonstrate that metadata as additional input to a convolutional network can improve segmentation results while being inexpensive in implementation as a nimble add-on to popular models. We hypothesize that this benefit of metadata can be attributed to facilitating multitask switching. This aspect of metadata-driven systems is explored and discussed in detail.

翻译：元信息是生物医学图像常见的伴随数据。然而，这种来自图像采集的潜在强大附加信号源，在深度学习方法中（尤其是语义分割任务中）的应用十分有限。本研究通过卷积网络中的通道调制机制整合元数据，并探究其对语义分割任务的影响。我们证明，将元数据作为卷积网络的额外输入能够改善分割结果，同时作为流行模型轻量级附加组件在实现上具有低成本优势。我们提出假设，元数据的这一优势可归因于其对多任务切换的促进作用。本文对元数据驱动系统的这一特性进行了深入探讨与详细分析。